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Register a new userDuoRAT: Towards Simpler Text-to-SQL Models
Duorat: نحو نماذج Simpler Text-to-SQL
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يمكن أن ترجمت نماذج النص العصبي المؤقتة مؤخرا لترجمة أسئلة اللغة الطبيعية بفعالية لاستعلامات SQL المقابلة على قواعد البيانات غير المرئية.العمل في الغالب على مجموعة بيانات العنكبوت، اقترح الباحثون حلولا متطورة بشكل متزايد للمشكلة.على عكس هذا الاتجاه، في هذه الورقة نركز على التبسيط.نبدأ بإعادة بناء DUORAT، وإعادة تنفيذ طراز Rat-Art-Art-Art الذي يعكس RAT-SQL باستخدام محولات العلاقات أو الفانيليا فقط كقطات بناء.نحن نؤدي العديد من التجارب الاجتثاث باستخدام Duorat كنموذج الأساس.تقوم تجاربنا بتأكيد فائدة بعض التقنيات وأشرح التكرار للآخرين، بما في ذلك ميزات وميزات SQL الهيكلية التي ترتبط بالسؤال مع المخطط.
Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, researchers have proposed increasingly sophisticated solutions to the problem. Contrary to this trend, in this paper we focus on simplifications. We begin by building DuoRAT, a re-implementation of the state-of-the-art RAT-SQL model that unlike RAT-SQL is using only relation-aware or vanilla transformers as the building blocks. We perform several ablation experiments using DuoRAT as the baseline model. Our experiments confirm the usefulness of some techniques and point out the redundancy of others, including structural SQL features and features that link the question with the schema.
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